Abstract

Within the profession of social work, the production and use of written texts is a high-stakes activity, playing a central role in all decisions around providing services for people, and at the same time used to evaluate social workers’ professional competence. Writing of all kinds pervades everyday social work practice, varying from case notes to inter-departmental emails to formal assessment reports. Despite the ‘writing-intensive’ nature of the profession (Lillis, Leedham & Twiner, in press), frequently, minimal attention is paid to writing in formal social work education programmes and professional training initiatives. Social work writing (often subsumed under the label of ‘recording’) is frequently the target of criticism in formal reviews and public media reporting of social work practice, hitting headline news when a case of extreme abuse or death occurs. Despite this, little empirical research has been carried out on these texts and, in particular, no corpus of professional UK social work writing exists. This gap is addressed by the ESRC-funded WiSP project (‘Writing in professional social work practice in a changing communicative landscape’, http://www.writinginsocialwork.com/) which brings corpus work together with discourse analysis in an ethnographically-framed, 3-year project. The specific focus of this presentation is on the corpus linguistic dimension of the research, and in particular what this reveals around the ‘core preoccupation’ of social workers’ writing.

The WiSP project has worked with five local authorities in the UK, exploring both the range of written texts produced and the writing practices of social workers. The newly-created WiSP corpus comprises 1 million words within 4,600 texts from the writing of 38 social workers within three UK local authorities in 2014-2017. The texts were written from the contexts of the domains of adult generic care, adult mental health care and children’s care and include the genres of assessment reports, casenotes and emails. The WiSP project also includes interview data (81 interviews with social workers and managers) and fieldnotes from 10 observation weeks.

In this presentation, we will explore findings from the whole WiSP corpus, following the widely-adopted corpus-assisted discourse studies methodology of keyness extraction followed by thematic classification of key items and further exploration of these items through collocates, concordance lines and close reading of whole texts (cf. Leedham, under review, and studies in Taylor and Marchi, 2018). The specific aim of this paper is to use corpus-assisted discourse studies to unpack the focus or core ‘preoccupation’ (Baker: 2010:26) of writing in social work. While we explore the corpus as a whole in this study, care is taken to note the three differing social work domains, and three main text categories. In addition to the corpus data, we draw on ethnographic insights from the additional WiSP datasets, as described above, and in particular we use detailed comments from 11 expert insiders, including social workers, social work educators and representatives from professional bodies.

Wmatrix corpus software (Rayson, 2008) was used to extract key items from the corpus using British English 2006 (BE06), a 1 million word corpus of published general written British English (Baker, 2009), as a reference corpus. The %DIFF effect size metric was used to establish keyness based on the size of the difference in the occurrence of items in two corpora (Gabrielatos, 2018). Items occurring in texts from fewer than five social workers were excluded as our focus here is on typicality rather than idiosyncracy (cf. McEnery, 2018). The resulting 226 key items were then manually categorised into thematic groups using an iterative process of analysing concordance lines and collocate lists (extracted using WordSmith Tools, Scott, 2017) combined with researcher close reading of text extracts. Thematic categories developed from the initial manual keyness analysis include ‘time and numbers’, ‘interpersonal’, ‘people, roles, activities and services’, ‘describing and evaluating’ and ‘communication’, with each category adding to our picture of the core ‘preoccupation’ of social work. In deciding on category names and assigning key items, we drew on our awareness of social worker discourse from the wider WiSP project.

Findings include confirmation of the extensive recording of communication exchanges, with differences in the ways social workers refer to their own and service users’ views, and in the use of quotations to provide evidence of service user views and actions. The extensive items in the ‘communication’ category signal the centrality of all types of communication in social work practice with some lexis indicating spoken interaction has taken place (e.g. call back, discuss, phone call, voicemail) and other key items signalling communication in writing (e.g. chronology, email, paperwork, uploaded). Examination of extended concordance lines reveals that social workers refer to their own communication differently to that of service users. Thus social workers advised, confirmed, discussed, enquired and requested where service users or their family members stated and, in discussion with social workers, agreed. In particular advised is used where the social worker is providing information to the service user rather than for the prototypical purpose of dispensing advice. In social work writing, stated and other verbs accorded to the service user or family are sometimes followed by a quotation (direct or indirect) from a service user or family member, providing evidence to support the writer’s commentary, showing the speaker’s strength of feeling and also perhaps showing some distance between the writer and the words quoted:

(1) [PERSON1] stated [PERSON2] would 'kick off' and I explained …
(2) [PERSON2] has told [PERSON1] to tell social care that the children are 'full of shit' and have lied about him.
Use of quotations in social work writing appears to function as a means of providing evidence on which the social worker can base their evaluations.

A common overarching theme within social work writing which comes through clearly in the keyness analysis is that of risk: with assessing, documenting and managing risk a core task of social work. Key items directly related to risk include concerns, high_risk, safeguarding, suffer, abusive, allegation, CSE (child sexual exploitation), and deteriorated.

Within the presentation we will discuss keyness analysis as a way of uncovering a set of ‘candidate professional lexis’ for further consideration. For example, social work writing tends to employ a particular set of lexis such as home environment (rather than home) and advised (rather than informed or told). Understanding and reflecting on such lexis may be helpful for newly-qualified social workers, social work students and educators. Controversy exists in social work around the use of what has been termed ‘jargon’, with calls for the word placement to be replaced by home and so on (e.g. Surviving Safeguarding, 2018). The issue of social work terminology arose in our consultation with expert insiders where we discussed the usefulness of professional language which succinctly encapsulates a range of meaning (e.g. educational establishment rather than nursery/primary/secondary/specialist schools and colleges) versus the potential for distancing the service user from social services through use of words such as contact rather than family time. The contribution of corpus analysis is to help to make visible the language within this ongoing debate, and we recognise there is a need for further exploration of candidate items through close reading and discussion with expert insiders.

The presentation will conclude with brief reflections on aspects of methodology, considering the interplay of manual and automated (Wmatrix) thematic categorization, and the usefulness of combining keyness analysis with additional data sources as carried out here.

Download history for this item

These details should be considered as only a guide to the number of downloads performed manually. Algorithmic methods have been applied in an attempt to remove automated downloads from the displayed statistics but no guarantee can be made as to the accuracy of the figures.